Though still in a proof-of-concept stage (presented at a "computer vision" conference in Miami today, June 22), the project makes use of 40 million GPS-tagged landmark photos from Google's Picasa and Panoramio and tour guide Web pages.

Next, we found candidate images for each landmark using these sources and Google Image Search, which we then "pruned" using efficient image matching and unsupervised clustering techniques. Finally, we developed a highly efficient indexing system for fast image recognition.

To get this cluster, Yagnik said Google entered the Web address of an untagged picture of a landmark into the recognition engine. The computer then identified and named it: "Recognized Landmark: Acropolis, Athens, Greece."

Google says the recognition engine is 80 percent accurate, which is pretty inaccurate by Google's standards. I'd guess Google will get it down to 98 percent accuracy if it can. Sergey Brin and Larry Page are notorious sticklers for speed and quality. Yagnik adds:

While we've gone a long way towards unlocking the information stored in text on the Web, there's still much work to be done unlocking the information stored in pixels. This research demonstrates the feasibility of efficient computer vision techniques based on large, noisy datasets.

Perhaps this is a fine exercise in separating the photographic wheat from the chaff. I see this landmark recognition image search feature eventually aiding middle schoolers' research projects for history.